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The purpose of this paper is to measure the comparative efficiency of 18 Korean commercial banks under the presence of negative observations and examine performance differences among them by grouping them according to their market conditions.

The authors employ two data envelopment analysis (DEA) models such as a Banker, Charnes, and Cooper (BCC) model and a modified slacks-based measure of efficiency (MSBM) model, which can handle negative data. The BCC model is proven to be translation invariant for inputs or outputs depending on output or input orientation. Meanwhile, the MSBM model is unit invariant in addition to translation invariant. The authors compare results from both models and choose one for interpreting results.

Most Korean banks recovered from the worst performance in 2011 and showed similar performance in recent years. Among three groups such as national banks, regional banks, and special banks, the most special banks demonstrated superb performance across models and years. Especially, the performance difference between the special banks and the regional banks was statistically significant. The authors concluded that the high performance of the special banks was due to their nationwide market access and ownership type.

This study demonstrates how to analyze and measure the efficiency of entities when variables contain negative observations using a data set for Korean banks. The authors have tried two major DEA models that are able to handle negative data and proposed a practical direction for future studies.

Although there are research papers for measuring the performance of banks in Korea, all of the papers in the topic have studied efficiency or productivity using positive data sets. However, variables such as net incomes and growth rates frequently include negative observations in bank data sets. This is the first paper to investigate the efficiency of bank operations in the presence of negative data in Korea.

To manage cash flow in supply chains, the purpose of this paper is to propose inverse data envelopment analysis (DEA) model.

This paper develops an inverse range directional measure (RDM) model to deal with positive and negative values. The proposed model is developed to estimate input and output variations such that not only efficiency score of decision making unit (DMU) remains unchanged, but also efficiency score of other DMUs do not change.

Given that auto making industry deals with huge variety and volumes of parts, cash flow management is so important. In this paper, inverse RDM models are developed to manage cash flow in supply chains. For the first time, the authors propose inverse DEA models to deal with negative data. By applying the inverse DEA models, managers distinguish efficient DMUs from inefficient ones and devise appropriate strategies to increase efficiency score. Given results of inverse integrated RDM model, other combinations of cash flow strategies are proposed. The suggested strategies can be taken into account as novel strategies in cash flow management. Interesting point is that such strategies do not lead to changes in efficiency scores.

In this paper, inverse input and output-oriented RDM model is developed in presence of negative data. These models are applied in resource allocation and investment analysis problems. Also, inverse integrated RDM model is developed.

This chapter develops a productivity analysis of the US pharmaceutical industry via Data Envelopment Analysis (DEA). This study concerns itself with 16 US pharmaceutical companies. The output variables are profit margin, operating margin, return on assets, and return on equity. The input variables are corporate workers and market capital. Since negative data appear in DEA, a directional distance approach was applied.

The purpose of this paper is to investigate the feasibility of using artificial neural networks (ANNs) in conjunction with data envelopment analysis (DEA) for the performance measurement of major mobile phone providers, and for subsequent predictions related to best performance benchmarking and decision making.

DEA and ANN are combined, providing an integrated modeling approach via a two-stage process. DEA is used for front end measurement, while ANN provides learning and prediction capabilities. DEA analysis of industry characteristics is based on the measurement of each decision-making unit's (DMU) performance. Back propagation neural networks (BPNN) can then predict each DMU's efficiency score, based on the results of the DEA models. Additional BPNN models provide best performance predictions.

The DEA module successfully evaluates the competitive status of firms in the mobile phone industry in terms of efficiency. Efficiency trends over the observation period reveal the dynamic nature of competition in this industry. The predictive power of the BPNN module has been demonstrated as well. The proposed system is an effective benchmarking and decision support tool, via its capability to simulate performance scenarios, thereby facilitating insightful, prudent decision making.

This paper proposes the use of two different but complementary methods, DEA and ANN, in a combined performance modeling approach, and examines mobile phone providers. This methodology can improve users’ performance benchmarking and decision-making processes. Additionally, adaptive prediction capability is provided through approximating efficient frontiers, in addition to performance measurement.

This paper aims to assess two distinct aspects of performance in terms of technical (sales) efficiency and efficiency in market value generation of a sample of Greek metallurgical firms listed on the Athens Exchange by using data envelopment analysis (DEA).

Both aspects of performance are measured by employing the DEA BCC model, combined with bootstrap and generalized proportional distance function (GPDF). Statistical analysis is performed to investigate whether there is a positive link between the two examined performance dimensions.

Inefficiency is uncovered in both performance dimensions, but there is a lower level of performance in market value generation than in technical efficiency. Correlation analysis results do not point out positive links between performance measures for the sample firms.

The derived performance measures allow firm managers to set their own priorities and to seek out improvements along the two dimensions of performance; moreover, they may contribute to the reduction of information asymmetry among investors.

This paper is one of a few that investigate the link between DEA-based sales performance and performance in market value generation. It contributes methodologically through the adoption of fundamental analysis principles in estimating efficiency in the two performance dimensions and the development of a DEA efficiency model in the presence of negative data.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

The purpose of the research presented in this paper is to provide a new approach related to the definition of variables in disaggregating branch expenses and income for evaluating the overall technical efficiency of bank branches by means of data envelopment analysis (DEA).

By applying an input minimization DEA model to a sample of bank branches of a large commercial bank in Greece, this study identifies pure technical and scale inefficiencies, efficiency and size relationship and returns to scale patterns. Moreover, it explores target setting strategies for inefficient branches.

Results indicate that branch size has an important influence on efficiency and that superior insights can be obtained by pure technical efficiency (PTE) and scale efficiency (SE), as constituent components of global technical efficiency (TE) than the information obtained from the analysis based on selected key performance indicators (KPIs) used by the bank under study.

A direction of future research would be to extend the analysis to incorporate environmental factors, such as branch location, local competitive environment, investment portfolio risk, among others, into the DEA assessment.

The study shows that DEA which provides an overall summary measure with respect to global TE and its constituent components PTE and SE can be used to complement the in‐house performance management system of the bank under study for the evaluation of its branch network.

From a policy perspective, this study highlights the use of DEA combined with statistical analysis to support the reduction of burden by means of either expense reduction or revenue enhancement.

Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. The purpose of this paper is to assess the market performance of the top retailers in the USA using 2007 operating data. It also aims to benchmark the performance of neuro‐intelligence models against traditional statistical techniques.

This paper uses neuro‐intelligence models to classify the relative efficiency of top USA retailers. Accuracy indices derived from the application of a non‐parametric data envelopment analysis approach are used to assess the classification accuracy of the models.

Results indicate that the neuro‐intelligence models are superior to traditional statistical methods. The paper also shows that the neuro‐intelligence models have a great potential for the classification of retailers' relative efficiency due to their robustness and flexibility of modeling algorithms.

The paper contributes practically and methodologically through the comparison of various parametric and non‐parametric techniques, which results in considerable information for business analysis.

The purpose of this paper is to develop a simultaneous measurement of overall performance and its two dimensions of efficiency and effectiveness in the case of Chinese construction industry.

A relational two-stage data envelopment analysis (DEA) method, which builds a relationship between component stages and can effectively identify inefficient stages, is developed and applied in order to measure overall performance, efficiency and effectiveness.

The construction industry of the Eastern region in China demonstrated the best results for overall performance, efficiency and effectiveness. The gaps between regions were primarily reflected in differences of pure technical efficiency. Performance indicators in the whole construction industry improved steadily and but could be improved more effectively. The coefficients of variation became smaller and more well-balanced across the whole industry.

Improving overall performance should focus on promoting construction efficiency at the project level and increasing management effectiveness at the company level. Sustainable development policies, which may include large investment and preferential policies, can narrow performance differences among the regions’ construction industries, and ultimately promote overall performance for the whole industry.

The relational two-stage DEA model is further developed in a variable returns-to-scale condition. The developed approach is generic and can provide a pathway for simultaneously measuring performance, efficiency and effectiveness and to recognise competitive advantages for promoting sustainable development.

Using data on 51 firms traded in the Egyptian Exchange from 2014 to 2016, this paper aimed to assess the efficiency of corporate governance (CG) and intellectual capital (IC) practices and to explore their influence on the probability of a firm's financial distress.

The relative efficiency of CG and IC practices has been measured under the Malmquist data envelopment analysis model. A modified Z-score model was applied to assess firms' financial distress.

The Wilcoxon signed-rank test revealed almost insignificant evidence regarding the improvement of CG and IC efficiency over the study period. The efficiency score of CG practices had no impact on the likelihood of financial distress. However, the efficiency score of IC negatively affected the probability of financial distress.

The integration of data envelopment analysis with Tobit regression was required for identifying the significant drivers of efficient CG and IC.

The findings shed light on the role of CG and IC in alleviating the degree of financial distress in Egypt as an emerging market, especially the need to raise firms' compliance with the Egyptian CG code from a voluntary to mandatory status.

This study, using Malmquist data envelopment analysis, is among the first attempts to assess the relative efficiency of CG and IC practices and their effects on financial distress.